Increasing Prosperity for the Poorest using Social Networks: Guest post by Kathryn Vasilaky

Social networks affect all of our lives; the people we know influence what we're exposed to and the actions we take. Gaining weight? Blame your network. Got a job promotion? Thank your network. In the developed world, the term "social networks" often illicits thoughts of Facebook, LinkedIn, Twitter, Instagram and many, many more. In the rural developing world, networks still depend, for the most part, on offline interactions. Access to information is by word of mouth, and a few central individuals often disseminate information from the top down to the remainder of a village. This is particularly true in agriculture—a necessary livelihood for most rural-bound individuals. And, because males are generally the higher performing producers, they are more central to village affairs and the targets for agricultural training and improvements. They are then expected to disseminate information outwards to the remainder of a village, but this rarely happens.

As a result, the gender gap, which we saw in a recent post, continues to grow, and women's production networks continue to suffer from large "structural holes," a self perpetuating cycle.

A recent study by myself and Kenneth Leonard aims to address this aspect of the productivity gap. What happens if (1) these holes were filled with new weak ties to other female farmers, who have new information to share? Would females learn as much if they were directly trained by extension agents? Would the best off or worst-off farmers improve their outcomes? How true is Granovetter's famous phrase, "the strength of weak ties?" Would females expand their networks, or do weak ties remain weak?

In a two-arm RCT in Northern and Eastern Uganda, we compare the effects of two methods of information dissemination for teaching cotton production. The first, a standard agricultural training program, involved bi-weekly extension agent visits. The second innovation was a social network based training program, where farmers taught their new farmer pair information that they were recently taught, and, hopefully, any other best practices that they had uncovered themselves. Think private tutor versus a friend of a friend of a friend (because your friend already taught you everything she knows) will teach you some algebra. Who will be more effective? Understand you? Be available? And cost less?

A key component to the study is that these new ties are randomly assigned, allowing the authors to overcome the usual "mirror effect" that plagues networks studies. It may appear that our networks influence our choice to watch "House of Cards," but, in reality, we and our networks look alike because we have similar interests—dramatics and politics—and face similar unobservable shocks—like this year's snowpocalypse. So our choices are bound to look alike even if we never observe our network's actions or base our actions off of theirs. Conversely, since the links are randomly assigned, they can safely estimate the effect of that new link.

What we find stands out. While others have documented that learning-from-others exists, networks size matters, and central actors are key for dissemination, we show that weak ties are a source of underutilized power for generating information flow. Most importantly, we show that the benefits from our methodology accrue to the very people that poverty programs target: the poorest performing individuals. And it just so happens that these individuals are also females. Conversely, the standard training improves outcomes for already high performing male farmers, which is also echoed by a recent study of Uganda's NAADS program (Kahubire, 2005; Lungahi and Opira, 2013).

As the Uganda government aims to decentralize their extension services, incorporating even some of the simple methodologies piloted by this study would be a cost effective and proven methodology to improve overall welfare, and increase shared prosperity for the very poorest farmers. Perhaps even President Underwood would support that.